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Field
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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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new nationwide AI system can be predicted using generated data sets of different sizes and measuring the environmental impact. This impact can be measured and calculated by our Software Energy Lab
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project will take a comprehensive approach, encompassing the design, manufacturing, and characterisation of metamaterial architectures for advanced radiation detection. The research will involve
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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. By integrating artificial intelligence (AI), multi-sensor fusion, and cognitive systems, the research will pioneer robust navigation architectures. These improvements are key to making future transport
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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exploring various architectures and unsupervised learning techniques to identify anomalies and diagnose specific fault types based on processed sensor data (e.g., vibrations, currents). Edge device deployment
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description Conduct a literature review on SSMs, LLM architectures, and hardware acceleration techniques. Investigating design choices that optimize the model across both the hardware and software stack. Design
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Hardware-software co-simulation and benchmarking This PhD project is part of SDU microelectronic unit’s effort in neuromorphic chip design and collaborates with international partners working on spiking AI
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We are offering a WASP, The Wallenberg AI, Autonomous Systems and Software Program, funded PhD position that provides a unique opportunity to develop deep expertise in robotics, machine learning